In this episode we will talk all about the various steps to transition to data science from non computer science backgrounds.

00:00 to 02:10: Introduction

02:11 to 06:00: My Background of moving to data science from electrical engineering

06:01 to 10:56: Steps 1 to 3 covering things like using external APIs, already processed datasets and performing full stack data science work

10:57 to 11:55: Break sponsored by Anchor

11:56: End: Steps 4 to 6 covering things like math and statistics, machine learning pipelines and data structures & algorithms

Some useful links:

1) Andrew Ng Deep Learning Specialization Coursera https://www.coursera.org/specializations/deep-learning

2) Intro to Statistics by Sebastien Thrun https://www.udacity.com/course/intro-to-statistics--st101

3) Aurelion Geron's book on machine learning https://www.amazon.com/dp/1491962291/?tag=omnilence-20

4) Pramp for mock algorithm sessions on video https://www.pramp.com/ 

5) Leetcode for algorithm question datasets https://leetcode.com/ 

Some great datasets to get started in machine learning: 

6) MNIST for hand written digits https://www.kaggle.com/c/digit-recognizer

7) Iris dataset for flower classification http://archive.ics.uci.edu/ml/datasets/iris

8) IMDB movie reviews https://ai.stanford.edu/~amaas/data/sentiment/

Thanks for listening! 

In this episode,  we cover the two main types of recommendation engines used at companies like Netflix and Spotify.

1) Content based recommendation systems use the genres or tags of each product to find other similar products to recommend to users.

We go through the pros and cons of each, the challenges, how do companies like Netflix and Spotify scale their recommendation engines for millions of users and more! 

My code in the Github repo which implements these concepts from scratch using MovieLens dataset.  

Links:

#netflix #spotify #movielens #recommendations #recommendation-engines

You and your team might spend weeks or even months building a model. These are the 3 mistakes to avoid in your next machine learning project! This can save you a lot of time and effort in your next project. 

These tips have been learnt from experiences deploying ML models in production as well as hearing from experts in the field. 

These tips and mistakes are typically not covered in traditional machine learning texts and courses, and that's why I really wanted to cover it in this podcast episode. I'd really appreciate your support for this podcast. Please visit the podcast webpage and support, so that I can continue to develop podcast episodes. Thanks for listening!

In this episode, we will talk all about what makes Flask such a great tool for both beginner and experienced data scientists to know. It was one of the first tools I learnt in my data science journey, and it has been so useful along the way. 

Flask is a micro-framework in Python which allows to build websites in a simple way. Flask will make you as a data scientist work better with the front end engineers. Also, it is a great way to build something like say recommender systems where, users can input a product they have liked, and you have a machine learning model in Python that reads this and recommends another product. 

Resources:

Thanks for listening to The Data Life Podcast!

To kick things off, I talk about the kind of topics you can expect to hear in this podcast. Welcome to The Data Life!